Put your open source track record to work from anywhere. You build realistic coding problems around bug fixes, new features, refactoring and performance work, each with a reproducible setup, a reference solution and a strict verifier. It suits a backend engineer who knows large codebases well.
What you will do
- Create challenging coding problems on feature work, bug fixes, refactoring and performance optimisation.
- Build a reproducible environment and a reference solution for each problem.
- Write deterministic verifiers that accept any reasonable valid solution and reject the rest.
- Track down defects and bottlenecks in varied codebases, and refactor legacy code for clarity and reliability.
- Document your reasoning and solution approach, and review code submitted by peers.
What you need
- Public open source contributions with a profile that shows them, such as GitHub, ideally in C++, Python, Java, Go, TypeScript or Rust. Without one, you take a technical assessment instead.
- Strong backend experience in Java, Node.js, Go, Rust, TypeScript, Python, C++ or similar, and a record of building scalable services and APIs.
- Comfort with debugging, performance work, automated testing and code review.
- A solid grasp of algorithms, data structures and computer science basics.
- No earlier AI experience is needed.
How pay works
The listed rate is $50-100 an hour, paid per task: every task that meets the project spec is paid. How long a task takes depends on your experience and workflow. A weekly minimum number of tasks applies.
How applying works
- Apply and answer the screening questions.
- Take a short AI interview, about 20 minutes.
- Take a technical assessment if your public profile does not fully show the skills the role needs.
- A hiring manager reviews your application.
Candidates who complete the AI interview for this role move through the process faster. After onboarding, selected people start their first tasks within 24-48 hours.
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